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Multivariate multipoint evaluation is the problem of evaluating a multivariate polynomial, given as a coefficient vector, simultaneously at multiple evaluation points. In this work, we show that there exists a deterministic algorithm for multivariate multipoint evaluation over any finite field \(\mathbb {F}\) that outputs the evaluations of an m-variate polynomial of degree less than d in each variable at N points in time, \(\begin{equation*} (d^m+N)^{1+o(1)}\cdot {{\sf poly}}(m,d,\log |\mathbb {F}|), \end{equation*}\) for all \(m\in \mathbb {N}\) and all sufficiently large \(d\in \mathbb {N}\). A previous work of Kedlaya and Umans (FOCS 2008 and SICOMP 2011) achieved the same time complexity when the number of variables m is at most \(d^{o(1)}\) and had left the problem of removing this condition as an open problem. A recent work of Bhargava, Ghosh, Kumar, and Mohapatra (STOC 2022) answered this question when the underlying field is not too large and has characteristic less than \(d^{o(1)}\). In this work, we remove this constraint on the number of variables over all finite fields, thereby answering the question of Kedlaya and Umans over all finite fields. Our algorithm relies on a non-trivial combination of ideas from three seemingly different previously known algorithms for multivariate multipoint evaluation, namely the algorithms of Kedlaya and Umans, that of Björklund, Kaski, and Williams (IPEC 2017 and Algorithmica 2019), and that of Bhargava, Ghosh, Kumar, and Mohapatra, together with a result of Bombieri and Vinogradov from analytic number theory about the distribution of primes in an arithmetic progression. We also present a second algorithm for multivariate multipoint evaluation that is completely elementary and, in particular, avoids the use of the Bombieri–Vinogradov theorem. However, it requires a mild assumption that the field size is bounded by an exponential tower in d of bounded height. More specifically, our second algorithm solves the multivariate multipoint evaluation problem over a finite field \(\mathbb {F}\) in time, \(\begin{equation*} (d^m+N)^{1+o(1)}\cdot {{\sf poly}}(m,d,\log |\mathbb {F}|), \end{equation*}\) for all \(m\in \mathbb {N}\) and all sufficiently large \(d\in \mathbb {N}\) , provided that the size of the finite field \(\mathbb {F}\) is at most \((\exp (\exp (\exp (\cdots (\exp (d)))))\) , where the height of this tower of exponentials is fixed. [ABSTRACT FROM AUTHOR]

Multipoint evaluation is the computational task of evaluating a polynomial given as a list of coefficients at a given set of inputs. Besides being a natural and fundamental question in computer algebra on its own, fast algorithms for this problem are also closely related to fast algorithms for other natural algebraic questions such as polynomial factorization and modular composition. And while nearly linear time algorithms have been known for the univariate instance of multipoint evaluation for close to five decades due to a work of Borodin and Moenck [7], fast algorithms for the multivariate version have been much harder to come by. In a significant improvement to the state-of-the-art for this problem, Umans [25] and Kedlaya & Umans [16] gave nearly linear time algorithms for this problem over field of small characteristic and over all finite fields, respectively, provided that the number of variables n is at most do(1) where the degree of the input polynomial in every variable is less than d. They also stated the question of designing fast algorithms for the large variable case (i.e., n x do(1)) as an open problem. In this work, we show that there is a deterministic algorithm for multivariate multipoint evaluation over a field Fq of characteristic p, which evaluates an n-variate polynomial of degree less than d in each variable on N inputs in time provided that p is at most do(1), and q is at most (exp(exp(exp(· · · (exp(d))))), where the height of this tower of exponentials is fixed. When the number of variables is large (e.g., n x do(1)), this is the first nearly linear time algorithm for this problem over any (large enough) field. Our algorithm is based on elementary algebraic ideas, and this algebraic structure naturally leads to the following two independently interesting applications: --We show that there is an algebraic data structure for univariate polynomial evaluation with nearly linear space complexity and sublinear time complexity over finite fields of small characteristic and quasipolynomially bounded size. This provides a counterexample to a conjecture of Miltersen [21] who conjectured that over small finite fields, any algebraic data structure for polynomial evaluation using polynomial space must have linear query complexity. We also show that over finite fields of small characteristic and quasipolynomially bounded size, Vandermonde matrices are not rigid enough to yield size-depth tradeoffs for linear circuits via the current quantitative bounds in Valiant's program [26]. More precisely, for every fixed prime p, we show that for every constant e > 0, and large enough n, the rank of any n × n Vandermonde matrix V over the field Fpa can be reduced to (n/exp(O(poly(e)log0.53 n))) by changing at most nT(e) entries in every row of V, provided a = poly(logn). Prior to this work, similar upper bounds on rigidity were known only for special Vandermonde matrices. For instance, the Discrete Fourier Transform matrices and Vandermonde matrices with generators in a geometric progression [9]. [ABSTRACT FROM AUTHOR]

Summary: Fault‐tolerance and self‐organization are critical properties in modern distributed systems. Self‐stabilization is a class of fault‐tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self‐stabilizing distributed algorithm for the 1‐MIS problem under the unfair central daemon assuming the distance‐3 model. Here, in the distance‐3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1‐MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is O(n)$$ O(n) $$ steps and the space complexity is O(logn)$$ O\left(\log n\right) $$ bits, where n$$ n $$ is the number of processes. Finally, we extend the notion of 1‐MIS to p$$ p $$‐MIS for each nonnegative integer p$$ p $$, and compare the set sizes of p$$ p $$‐MIS (p=0,1,2,...$$ p=0,1,2,\dots $$) and the maximum independent set. [ABSTRACT FROM AUTHOR]

This article proposes an optimal approach of improving the dynamic stability in power system by using hybrid method based power system-stabiliser. The proposed hybrid approach is an integration of Owl Search algorithm (OSA) and Improved Grey Wolf Optimizer (IGWO); hence, it is known as OSA-IGWO method. The objective of the proposed method is to enhance the power system's dynamic stability. This notion offers a singular approach to design the sturdy Power System Stabiliser (PSS) and enhance the dynamic energy machine balance. The Modified Heffron-Phillip's version can be evolved through generator facet transformer voltage because endless bus voltage and connection are considered to lessen the complexity with computational time. An energy machine stabiliser is referred to as Modified Power System Stabiliser (MPSS) that can be evolved and incorporated with PID controller; the usage of proposed approach in a Single-Machine-Infinity Bus-System uses Modified Heffron-Phillips (MHP) version. The OSA-IGWO approach may be used to select the benefit sets of PID-MPSS. The proposed version complements the dynamic overall performance of machine via the way of means of growing the damping ratio of the energy machine below diverse running conditions. The OSA-IGWO method is done in MATLABplatform, and its performance is compared with existing methods. [ABSTRACT FROM AUTHOR]

In recent years, the occurrence of task failures are becoming prevalent in cloud computing due to various factors such as the increasing complexity of cloud environments, heterogeneity of resources, resource limitations and inadequate allocation. Task failure due to insufficient allocation poses a significant challenge in cloud computing. When tasks are not allocated effectively, they may not be completed within their deadlines which ultimately leads to failure. Hence, effective allocation strategies combined with appropriate fault tolerance measures are vital for addressing these challenges and mitigating the risk of task failures. This paper proposes a fault-tolerant task allocation algorithm (FTTA) for independent tasks with deadline through preemptive migration in heterogeneous cloud environments to reduce task failure. The proposed algorithm involves three phases: the initial phase decides the priority of tasks in the ready list to minimize the execution time and meet task deadlines, the second phase includes the selection of a suitable virtual machine with minimum execution time and the last phase assigns task on available or non-available (which may available in future) virtual machines to find the best execution time within the deadline limit. During the task allocation process, the algorithm adopts fault-tolerant strategy that includes preemptive migration if necessary which allows the migration of tasks to identify the best suitable virtual machine. An analysis of the proposed algorithm reveals that the overall time complexity is O (n log n + n m 2) where n is the number of tasks and m is the number of virtual machines. Further, the performance of the algorithm is evaluated for different sets of tasks (small to large) while varying the number of virtual machines. The experimental results demonstrate that FTTA outperforms First Come First Served (FCFS), Priority based algorithm, Shortest Job First (SJF), Dynamic Maximum Minimum (Dy max min) and RADL algorithms in terms of number of rejected tasks, makespan, speedup and efficiency. [ABSTRACT FROM AUTHOR]

Crater identification aims to match the craters of a planetary body observed in an image to the corresponding entries in a catalogue of known craters of the same body. It is a key step in crater-based navigation algorithms for planetary missions. Many crater identification methods extract descriptors from the observed craters and compare them to an index of descriptors constructed a priori from the crater catalogue. The need for viewpoint invariance motivates descriptors defined over crater tuples (e.g. , crater triads for projective invariance). However, this implies a descriptor index that grows rapidly (e.g. , cubically) with the size of the catalogue, which practically limits the spatial resolution and/or coverage of the catalogue. Another serious drawback is the sensitivity of the descriptors and the matching accuracy to noise and outliers in the crater detection result, which are inevitable due to imperfect crater detection algorithms. In this paper, we propose a novel descriptorless crater identification technique. At its core, our method solves the perspective cone alignment problem with geometric verification iteratively over the crater catalogue. We show that our simple algorithm, with time and space complexities that are linear in the catalogue size, is substantially more accurate than state-of-the-art descriptor-based methods in the presence of noise. Moreover, the availability of multi-core onboard processors raises the prospect of speeding up our method through parallelisation. • This paper introduces the first descriptor-less crater identification method. • It comprises a perspective cone alignment solver and a geometric verification scheme. • It achieves 2–3 orders of magnitude memory improvement over descriptor-based methods. • It demonstrated superior robustness against input noise and challenging conditions. [ABSTRACT FROM AUTHOR]

In this paper, the approximate parallel prefix adders with minimizing hardware and timing complexities are proposed. Moreover, an approximate multiplier based on these adders is designed. The approximate structures include two approximate Sklansky adders, one approximate Ladner-Fischer adder, and one approximate Kogge-Stone adder. The proposed adders are free from carry rippling. The main strategy for approximate design is primarily based on rearranging and deleting sub-blocks and secondary reducing the critical path delay and area in the adders. In this case, we have a trade-off between accuracy, delay, and area. The proposed approximate multiplier has a serial structure that is designed based on using one approximate parallel prefix adder. The proposed approximate adders and multiplier are compared from hardware and accuracy point of view such as gate count, delay, area delay product, error rate, mean error distance, mean relative error distance, and normalized error distance. The efficacy of proposed structures in image processing applications such as image smoothing (low-pass filter) and image multiplication is performed using MATLAB. The results show the proposed approximate structures are comparable in terms of area, delay, PSNR, and mean structural similarity index metric parameters with other works. [ABSTRACT FROM AUTHOR]

Diagnosis of depression is based on tests performed by psychiatrists and information provided by patients or their relatives. In the field of machine learning (ML), numerous models have been devised to detect depression automatically through the analysis of speech audio signals. While deep learning approaches often achieve superior classification accuracy, they are notably resource-intensive. This research introduces an innovative, multilevel hybrid feature extraction-based classification model, specifically designed for depression detection, which exhibits reduced time complexity. MODMA dataset consisting of 29 healthy and 23 Major depressive disorder audio signals was used. The constructed model architecture integrates multilevel hybrid feature extraction, iterative feature selection, and classification processes. During the Hybrid Handcrafted Feature (HHF) generation stage, a combination of textural and statistical methods was employed to extract low-level features from speech audio signals. To enhance this process for high-level feature creation, a Multilevel Discrete Wavelet Transform (MDWT) was applied. This technique produced wavelet subbands, which were then input into the hybrid feature extractor, enabling the extraction of both high and low-level features. For the selection of the most pertinent features from these extracted vectors, Iterative Neighborhood Component Analysis (INCA) was utilized. Finally, in the classification phase, a one-dimensional nearest neighbor classifier, augmented with ten-fold cross-validation, was implemented to achieve detailed, results. The HHF-based speech audio signal classification model attained excellent performance, with the 94.63 % classification accuracy. The findings validate the remarkable proficiency of the introduced HHF-based model in depression classification, underscoring its computational efficiency. • The research introduces a novel depression detection model using multilevel hybrid feature extraction from speech audio signals. • The study employs the MODMA dataset, containing audio signals from individuals with and without Major Depressive Disorder. • Advanced techniques like Hybrid Handcrafted Feature generation and Multilevel Discrete Wavelet Transform are used for effective feature extraction. • The model achieves a high classification accuracy of 94.63%, demonstrating both effectiveness and computational efficiency in depression diagnosis. [ABSTRACT FROM AUTHOR]

Self-sufficient sensors scattered over different regions of the world comprise distributed wireless sensor networks (DWSNs), which track a range of environmental and physical factors such as pressure, temperature, vibration, sound, motion, and pollution. The use of data fusion becomes essential for combining information from various sensors and system performance. In this study, we suggested the multi-class support vector machine (SDF-MCSVM) with synthetic minority over-sampling techniques (SMOTE) data fusion for wireless sensor network (WSN) performance. The dataset includes 1,334 instances of hourly averaged answers for 12 variables from an AIR quality chemical multisensor device. To create a balanced dataset, the unbalanced data was first pre-processed using the SMOTE. The grey wolf optimization (GWO) approach is then used to reduce features in an effort to improve the efficacy and efficiency of feature selection procedures. This method is applied to classify the fused feature vectors into multiple categories at once to improve classification performance in WSNs and address unbalance datasets. The result shows the proposed method reaches high precision, accuracy, F1-score, recall, and specificity. The computational complexity and processing time were decreased in the study by using the proposed method. This is great potential for accurate and timely data fusion in dispersed WSNs with the successful integration of data fusion technologies. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms, especially evolutionary algorithms, have been frequently used to find near optimal solutions to combinatorial optimization problems. The evaluation of such algorithms is often conducted through comparisons with other algorithms on a set of benchmark problems. However, even if one algorithm is the best among all those compared, it still has difficulties in determining the true quality of the solutions found because the true optima are unknown, especially in dynamic environments. It would be desirable to evaluate algorithms not only relatively through comparisons with others, but also in absolute terms by estimating their quality compared to the true global optima. Unfortunately, true global optima are normally unknown or hard to find since the problems being addressed are NP-hard. In this paper, instead of using true global optima, lower bounds are derived to carry out an objective evaluation of the solution quality. In particular, the first approach capable of deriving a lower bound for dynamic capacitated arc routing problem (DCARP) instances is proposed, and two optimization algorithms for DCARP are evaluated based on such a lower bound approach. An effective graph pruning strategy is introduced to reduce the time complexity of our proposed approach. Our experiments demonstrate that our approach provides tight lower bounds for small DCARP instances. Two optimization algorithms are evaluated on a set of DCARP instances through the derived lower bounds in our experimental studies, and the results reveal that the algorithms still have room for improvement for large complex instances. [ABSTRACT FROM AUTHOR]

Linear cryptanalysis is one of the most classical cryptanalysis methods for block ciphers. Some critical techniques of the key-recovery phase are developed for enhancing linear cryptanalysis. Collard et al. improved the time complexity for last-round key-recovery attacks by using FWT. A generalized key-recovery algorithm for an arbitrary number of rounds with an associated time complexity formula is further provided by Flórez-Gutiérrez and Naya-Plasencia based on FWT in Eurocrypt 2020. However, the previous generalized algorithms are mainly applied to block ciphers with SPN structures, where the round-keys in the first and last round XORed to the state can be easily defined as outer keys. In Asiacrypt 2021, Leurent et al. applied the algorithm by Flórez-Gutiérrez et al. to Feistel structure ciphers. However, for other structures, such as NLFSR-based, the outer keys can not be directly deduced to utilize the previous algorithms. This paper extends the algorithm by Flórez-Gutiérrez et al. for more complicated structures, including but not limited to NLFSR-based, Feistel, ARX, and SPN. We also use the dependency relationships between ciphertext, plaintext and key information bits to eliminate the redundancy calculation and the improve analysis phase. We apply the algorithm with the improved analysis phase to KATAN (NLFSR-based) and SPARX (ARX). We obtain significantly improved results. The linear results we find for SPARX-128/128 beat other cryptanalytic techniques, becoming the best key recovery attacks on this cipher. The previous best linear attacks on KATAN32, KATAN48 and KATAN64 are improved by 9, 4, and 14 rounds, respectively. [ABSTRACT FROM AUTHOR]

3D printing technology proves itself to be effective in the field of medical industry due to processing potential of titanium alloys. Nonetheless it also has its drawbacks, the most severe being high roughness of printed elements’ area as well as the need to remove support structures created following the printing. Mechanical processing is commonly used for said parameters being enhanced. The completion of that process, however, takes a lot of time and prevents hard-to-reach elements from being reached. The task of this article is to provide a new method of firming the print’s surface and removing load-bearing structures. To achieve this, selective laser melting (SLM) technology will be used along with bathing prints in HF/HNO3 solution, all of which are supported by ultrasound. This process will enhance the material fatigue processing while reducing the postprocessing time and complexity. [ABSTRACT FROM AUTHOR]

Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of O (N 2 log (N)) , superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification. [ABSTRACT FROM AUTHOR]

Currently, specialists and researchers are focusing their efforts on information security, while frequently exchanged data is increasingly vulnerable to cyberattacks and threats. They use cryptography and watermarking methods to ensure copyright ownership protection, authenticity and integrity by hiding secret data within encrypted images. Most existing watermarking schemes are unable to ensure a good balance between imperceptibility, robustness, time processing, blindness and security. To address these problems, we propose a novel blind watermarking scheme for color images based on chaotic sequence, A-start algorithm and DCT. First, we apply DCT transform on the host image to produce DCT weights. The image block sequences are then slightly updated and scrambled from the obtained DCT weights using A-start algorithm and Arnold chaotic map. Finally, the embedded bits are extracted using the same embedding steps. Optimized image block sequences result in imperceptible, secure and robust watermark bits while reducing processing time. Experimental results show that the proposed approach outperforms other similar works when it comes to imperceptibility exceeding 74 dB and execution time less than 1.5 s for both watermark embedding and extraction. It has exceptional robustness to filtering, rotation and JPEG compression attacks with normalized correlations exceeding 0.8. [ABSTRACT FROM AUTHOR]

Physical unclonable functions (PUFs) have emerged as a favorable hardware security primitive, they exploit the process variations to provide unique signatures or secret keys amidst other critical cryptographic applications. CMOS-based PUFs are the most popular type, they generate unique bit strings using process variations in semiconductor fabrication. However, most existing CMOS PUFs are found to be vulnerable to modeling attacks based on machine learning (ML) algorithms. Memristors leveraging nanotechnology fabrication processes and highly nonlinear behavior became an interesting alternative to the existing CMOS-based PUF technology, introducing cryptographic and resilient random outputs. Memristor-based PUFs are emerging due to the inherent randomness at both the memristor level due to the cycle-to-cycle (C2C) programming variation of the device and the fabrication process level such as the cross-sectional area and variations. Our study focuses on building a machine learning analysis and attack framework of tools on C u / H f O 2 - x / p + + S i memristor-based PUF (MR-PUF). Our objective is to test the resiliency of the security margins of the presented PUF using machine learning analysis tools, on-top of holistic NIST cryptographic randomness testing initially provided, to provide a high level of certainty in predicting the randomness output of the verified Memrister-based PUF. Our main contribution is a holistic study that focuses on attacking the randomness output resiliency based on building randomness predictors using Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Mixture Models (GMM), K-means, K-means + + , Random Forest, XGBoost and LSTM, within efficient time, and data complexity. Our results yield low accuracy and ROC results of within 0.49 - 0.52 and 0.49 - 0.52 respectively, indicating failure in predicting random data demonstrates efficient randomness prediction resiliency of the MR-PUF. The efficient time and data complexities of these attacks illustrated in this study are yielded to be linear and quadratic resulting in attack execution time in seconds and 5032 training samples combined with 2157 testing samples to verify the randomness of PUF. [ABSTRACT FROM AUTHOR]

In order to perform emotion prediction in music quickly and accurately, we have proposed a muscular and tall neural network architecture for music emotion classification. Specifically, during the audio pre-processing stage, we converge mel-scale frequency cepstral coefficients features and residual phase features with weighting, enabling the extraction of more comprehensive music emotion characteristics. Additionally, to enhance the accuracy of predicting musical emotion while reducing computational complexity during training phase, we consolidate Long short term memory network with Broad learning system network. We employ long short term memory structure as the feature mapping node of broad learning system structure, leveraging the advantages of both network models. This novel Neural Network architecture, called BLNN (Broad-Long Neural Network), achieves higher prediction accuracy. i.e., 66.78%, than single network models and other benchmark with/without consolidation methods. Moreover, it achieves lower time complexity than other excellent models, i.e., 169.32 s of training time and 507.69 ms of inference time, and achieves the optimal balance between efficiency and performance. In short, the extensive experimental results demonstrate that the proposed BLNN architecture effectively predicts music emotion, surpassing other models in terms of accuracy while reducing computational demands. In addition, the detailed description of the related work, along with an analysis of its advantages and disadvantages, and its future prospects, can serve as a valuable reference for future researchers. [ABSTRACT FROM AUTHOR]

Although existing in situ oceanographic data are sparse, such data still play an important role in submarine monitoring and forecasting. Considering budget limitations, an efficient spatial sampling scheme is critical to obtain data with much information from as few sampling stations as possible. This study improved existing sampling methods based on the Quadtree (QT) algorithm. In the first-phase sampling, the gradient-based QT (GQT) algorithm is recommended since it avoids the repeated calculation of variance in the Variance QT (VQT) algorithm. In addition, based on the GQT algorithm, we also propose the algorithm considering the change in variation (the GGQT algorithm) to alleviate excessive attention to the area with large changes. In second-phase sampling, QT decomposition and the greedy algorithm are combined (the BG algorithm). QT decomposition is used to divide the region into small blocks first, and then within the small blocks, the greedy algorithm is applied to sampling simultaneously. In terms of sampling efficiency, both the GQT (GGQT) algorithm and the BG algorithm are close to the constant time complexity, which is much lower than the time consumption of the VQT algorithm and the dynamic greedy (DG) algorithm and conducive to large-scale sampling tasks. At the same time, the algorithms recommend above share similar qualities with the VQT algorithm and the dynamic greedy algorithm. [ABSTRACT FROM AUTHOR]

In this study, we analyze the changes over time in the complexity and structure of words used in article titles and the connections between articles in citation networks, focusing on the topic of artificial intelligence (AI) up to 2020. By measuring unpredictability in word usage and changes in the connections between articles, we gain insights into shifts in research focus and diversity of themes. Our investigation reveals correspondence between fluctuations in word complexity and changes in the structure of citation networks, highlighting links between thematic evolution and network dynamics. This approach not only enhances our understanding of scientific progress but also may help in anticipating emerging fields and fostering innovation, providing a quantitative lens for studying scientific domains beyond AI. [ABSTRACT FROM AUTHOR]

In this paper, we address the challenge of creating candidate sets for large-scale Traveling Salesman Problem (TSP) instances, where choosing a subset of edges is crucial for efficiency. Traditional methods for improving tours, such as local searches and heuristics, depend greatly on the quality of these candidate sets but often struggle in large-scale situations due to insufficient edge coverage or high time complexity. We present a new heuristic based on fuzzy clustering, designed to produce high-quality candidate sets with nearly linear time complexity. Thoroughly tested on benchmark instances, including VLSI and Euclidean types with up to 316,000 nodes, our method consistently outperforms traditional and current leading techniques for large TSPs. Our heuristic's tours encompass nearly all edges of optimal or best-known solutions, and its candidate sets are significantly smaller than those produced with the POPMUSIC heuristic. This results in faster execution of subsequent improvement methods, such as Helsgaun's Lin–Kernighan heuristic and evolutionary algorithms. This substantial enhancement in computation time and solution quality establishes our method as a promising approach for effectively solving large-scale TSP instances. [ABSTRACT FROM AUTHOR]

Existing methods to obtain position from inertial sensors typically use a combination of multiple sensors and orientation modeling; thus, obtaining position from a single inertial sensor is highly desirable given the decreased setup time and reduced complexity. The dead reckoning method is commonly chosen to obtain position from acceleration; however, when applied to upper limb tracking, the accuracy of position estimates are questionable, which limits feasibility. A new method of obtaining position estimates through the use of zero velocity updates is reported, using a commercial IMU, a push-to-make momentary switch, and a 3D printed object to house the sensors. The generated position estimates can subsequently be converted into sound through sonification to provide audio feedback on reaching movements for rehabilitation applications. An evaluation of the performance of the generated position estimates from a system labeled 'Soniccup' is presented through a comparison with the outputs from a Vicon Nexus system. The results indicate that for reaching movements below one second in duration, the Soniccup produces positional estimates with high similarity to the same movements captured through the Vicon system, corresponding to comparable audio output from the two systems. However, future work to improve the performance of longer-duration movements and reduce the system latency to produce real-time audio feedback is required to improve the acceptability of the system. [ABSTRACT FROM AUTHOR]

This paper proposed an aggregate signature scheme based on the SM9 algorithm to address the issue of excessive storage space occupied by the signatures of n messages generated by the conventional SM9 signature scheme. This scheme reduced the time cost of verifying multiple signatures compared to the original SM9 scheme, with a space cost of about 66.7% of the original SM9 scheme. Furthermore, the scheme introduced a locally verifiable approach based on the SM9 aggregate signature to tackle the problem where validators need only verify the correctness of specific messages when verifying signatures in current aggregate signature algorithms but still require knowledge of the complete message list. For the aggregated signatures S of a messages generated by a single user, the signer generated verification tags for a specific message m, enabling the verifier to verify the correctness of the message's signature without knowledge of the complete message list. Theoretical and experimental analysis confirm that the proposed scheme achieves a time complexity of O(1) for verifying specific messages given an aggregated signature. [ABSTRACT FROM AUTHOR]

The imaging quality of ultrasonic phased array technology significantly affects the quantification of small defects inside metals. Moreover, traditional manual evaluation methods require a substantial amount of manpower and time, and they are easily influenced by subjective factors. To mitigate the impact of imaging quality on the automated quantification of defects, this study proposes an improved gcForest algorithm (AWGA-gcForest, Adaptive window genetic algorithm- gcForest). Firstly, the acquired ultrasonic full matrix data from the phased array is processed using full focusing A-scan technique. Additionally, a window averaging weighting method is introduced for ultrasound signal processing to eliminate the interference from incident waves and backwall echoes. Subsequently, a multi-scale adaptive sliding window adjustment strategy based on Mean Absolute Deviation (MAD) is employed to reduce the computational time complexity of the gcForest algorithm. Furthermore, a weighted approach optimised with a genetic algorithm is utilised in the cascade forest to improve the classification accuracy and generalisation capability. Experimental results demonstrate that the proposed method achieves an accuracy of 97.50%, precision of 97.26%, recall rate of 96.63%, and F1 score of 96.92, exhibiting higher detection accuracy compared to other models while also reducing time costs. [ABSTRACT FROM AUTHOR]

Plant Leaf Disease (PLD) detection is helpful for several fields like Agriculture Institute and Biological Research. The country’s economic growth depends on the productivity of the agricultural field. Recently developed models based on deep learning give more accurate and precise results over the detection and classification of PLD while evolving through image processing approaches. Many image-processing approaches are used for the identification and classification of PLD. The quality of agricultural products is mainly affected by several factors like fungi, bacteria, and viruses. These factors severely destroy the entire growth of the plant. Hence, some outperformed models are needed to detect and identify the severity level of plant diseases yet, the identification requires more time and has a struggle to identify the appropriate type of disease based on its symptoms. Therefore, several automatic detection and classification models are developed to avoid the time complexity. Computerized image processing approaches are utilized for crop protection, which analyzes the color information of leaves from the collected images. Hence, image processing techniques play an important role in the identification and classification of PLD. It gives more advantages by lowering the task of illustrating crops on large farms and detecting the leaf diseases at the initial stage itself based on the symptoms of the plant leaves. While implementing a new model, there is a need to study various machine and deep learning-based structures for PLD detection approaches. This research work provides an overview of various heuristic approaches, machine learning, and deep learning models for the detection and classification of PLD. This research work also covers the various constraints like PLD detection tools, performance measures, datasets used, and chronological review. Finally, the research work explores the research findings and also the research gaps with future scope. [ABSTRACT FROM AUTHOR]

By considering maps and routes as sequences of line segments, their intersections can be computed to find out useful information like the possibility of collision in a military area where the parties do not trust each other. At the first glance, finding the coordinates of the intersections is seemed impossible to be solved securely since having coordinates of two intersection points on the same line reveals the passing line. In this paper, we solve this problem by suggesting a secure two-party protocol in presence of passive adversaries. Additionally, regarding the fact that in some cases, the fixedness of the inputs of the parties in classic security models is an unrealistic assumption, we define the new concept of input-adaptive security and show that our method is secure against such an adversary who is able to select his inputs adaptively. In addition to serve different approaches like oblivious transfer and sometimes homomorphic encryption, we also employ some tricks to prevent the distribution of harmful information between specific parties to achieve our intended security level. We provide formal proofs to show the security of our protocol. Time complexity analysis and implementations show that our protocol finds the intersections in feasible time of O (n log n) and indicate that our protocol is as good as the unsecure optimal method of line segment intersection computation. In comparison, previous methods require O (n 2) to only detect the existence of intersection between two sets of n line segments and are unable to find the coordinates of the intersections. [ABSTRACT FROM AUTHOR]

In a market, there are many groups caused by geographical location or other reasons, and consumers in groups have their own brand preferences for a type of products. The diversity of consumers' brand preferences will avoid the phenomenon of brand simplification and monopoly caused by consistent brand reference of consumers, and to some extent, promote the prosperity and development of various brands in each group. For brand diversification in a market, we hope the brand preferences of consumers in each groups are as equilibrious as possible, so Influence Maximization under Equilibrious Groups (IMEG) is proposed to select k nodes to maximize the number of equilibrious groups after information diffusion. This paper proves that the IMEG is NP-hard, and computing the objective function is #P-hard. It also proves that the objective function is neither submodular nor supermodular under Independent Cascade (IC) model and Linear Threshold (LT) model. Then, the Equilibrious Groups Maximization Solution (EGMS) algorithm is presented to solve our problem. And by comparing with baseline algorithms using different datasets (dolphins, wildbird, weaver and hamsterster), it can be found that the EGMS has obvious advantages in time complexity and spatial complexity. In particular, the running time of EGMS is about 3.7 × 10 - 2 , 2.4 × 10 - 2 , 1.1 × 10 - 1 and 1.8 × 10 - 3 times that of the fastest baseline algorithm in dolphins, wildbird, weaver and hamsterster respectively. And the experiments in small-world, scale-free, random and regular network verify the robustness of EGMS with the varying parameters, such as the number of nodes, the probability of adding edges and the number of adjacencies. [ABSTRACT FROM AUTHOR]

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP–ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases. [ABSTRACT FROM AUTHOR]

Analog circuits composed by large number of nodes in a tightly coupled structure pose significant challenges due to their prohibitive CPU simulation time. This work describes a method to speed up the simulation of such circuits by means of the combination of space state formulation of circuit equations with explicit integration methods parallelized over a many-core processor such as a GPU. Although stability of explicit techniques require smaller integration steps compared to implicit methods, the proposed method employs a fast estimate of the maximum allowed step size to guarantee numerical stability, which yields a shorter simulation time for increasing complexity circuit architectures. Moreover, the proposed technique can be straightforward parallelized on a many core architecture. The proposed method is demonstrated with two examples using constant and variable coefficients respectively: an RLC interconnect and a MOS-C network to perform Gaussian filtering of medium resolution images. The results obtained have been compared to a parallel version of SPICE and show improvements up to two orders of magnitude for transient simulations depending of the circuit size. [ABSTRACT FROM AUTHOR]

This paper proposes an encryption algorithm for crucial areas of a video based on chaos and a neural network, which SVEA (Selective Video Encryption Algorithm). The critical areas of each frame in a video are extracted by deep learning to the encryption system. A one-step encryption algorithm is used to encrypt these critical areas based on chaos, where scrambling and diffusion are simultaneously performed. A new chaotic system 2D extended Schaffer function map (2D-ESFM) is utilized in the encryption system, inspired by the Schaffer function. The system has demonstrated excellent performance through Lyapunov exponents (LEs), permutation entropy (PE), the 0-1 test, and other methods. Additionally, to resist chosen plaintext attacks, the secret key is generated by a neural network, with the critical areas of the video as inputs to the neural network. The chaotic system generates the biases and weights for the neural network. We evaluate SVEA on our dataset (Gymnastics at the Olympic Games) and public datasets. SVEA exhibits strong security characteristics compared to state-of-the-art algorithms and reduces time complexity by approximately 51.3%. • An encryption algorithm for critical areas of a video is proposed. • A 2D extended Schaffer function map (2D-ESFM) with a larger parameter space and a better performance is proposed. • The secret key of the cryptosystem is generated by the neural network, and a one-step encryption method is proposed. • A dataset is built about video encryption, called "Gymnastics at the Olympic Games". [ABSTRACT FROM AUTHOR]

A minimal separator of a graph G is a set S⊆V(G) such that there exist vertices a,b∈V(G)⧹S with the property that S separates a from b in G, but no proper subset of S does. For an integer k≥0, we say that a minimal separator is k‐simplicial if it can be covered by k cliques and denote by Gk the class of all graphs in which each minimal separator is k‐simplicial. We show that for each k≥0, the class Gk is closed under induced minors, and we use this to show that the Maximum Weight Stable Set problem can be solved in polynomial time for Gk. We also give a complete list of minimal forbidden induced minors for G2. Next, we show that, for k≥1, every nonnull graph in Gk has a k‐simplicial vertex, that is, a vertex whose neighborhood is a union of k cliques; we deduce that the Maximum Weight Clique problem can be solved in polynomial time for graphs in G2. Further, we show that, for k≥3, it is NP‐hard to recognize graphs in Gk; the time complexity of recognizing graphs in G2 is unknown. We also show that the Maximum Clique problem is NP‐hard for graphs in G3. Finally, we prove a decomposition theorem for diamond‐free graphs in G2 (where the diamond is the graph obtained from K4 by deleting one edge), and we use this theorem to obtain polynomial‐time algorithms for the Vertex Coloring and recognition problems for diamond‐free graphs in G2, and improved running times for the Maximum Weight Clique and Maximum Weight Stable Set problems for this class of graphs. [ABSTRACT FROM AUTHOR]

With the widespread adoption of mobile internet, users generate vast amounts of location-based data across multiple social networking platforms. This data is valuable for applications such as personalized recommendations and targeted advertising. Accurately identifying users across different platforms enhances understanding of user behavior and preferences. To address the complexity of cross-domain user identification caused by varying check-in frequencies and data precision differences, we propose HTEGAT, a hierarchical trajectory embedding-based graph attention network model. HTEGAT addresses these issues by combining an Encoder and a Trajectory Identification module. The Encoder module, by integrating self-attention mechanisms with LSTM, can effectively extract location point-level features and accurately capture trajectory transition features, thereby accurately characterizing hierarchical temporal trajectories. Trajectory Identification module introduces trajectory distance-neighbor relationships and constructs an adjacency matrix based on these relationships. By utilizing attention weight coefficients in a graph attention network to capture similarities between trajectories, this approach reduces identification complexity while addressing the issue of dataset sparsity. Experiments on two cross-domain Location-Based Social Network (LBSN) datasets demonstrate that HTEGAT achieves higher hit rates with lower time complexity. On the Foursquare-Twitter dataset, HTEGAT significantly improved hit rates, surpassing state-of-the-art methods. On the Instagram-Twitter dataset, HTEGAT consistently outperformed contemporary models, showcasing its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

In this paper, we propose systematic approaches for learning imbalanced data based on a two-regime process: regime 0, which generates excess zeros (majority class), and regime 1, which contributes to generating an outcome of one (minority class). The proposed model contains two latent equations: a split probit (logit) equation in the first stage and an ordinary probit (logit) equation in the second stage. Because boosting improves the accuracy of prediction versus using a single classifier, we combined a boosting strategy with the two-regime process. Thus, we developed the zero-inflated probit boost (ZIPBoost) and zero-inflated logit boost (ZILBoost) methods. We show that the weight functions of ZIPBoost have the desired properties for good predictive performance. Like AdaBoost, the weight functions upweight misclassified examples and downweight correctly classified examples. We show that the weight functions of ZILBoost have similar properties to those of LogitBoost. The algorithm will focus more on examples that are hard to classify in the next iteration, resulting in improved prediction accuracy. We provide the relative performance of ZIPBoost and ZILBoost, which rely on the excess kurtosis of the data distribution. Furthermore, we show the convergence and time complexity of our proposed methods. We demonstrate the performance of our proposed methods using a Monte Carlo simulation, mergers and acquisitions (M&A) data application, and imbalanced datasets from the Keel repository. The results of the experiments show that our proposed methods yield better prediction accuracy compared to other learning algorithms. [ABSTRACT FROM AUTHOR]

Semantic code clone detection is to find code snippets that are structurally or syntactically different, but semantically identical. It plays an important role in software reuse, code compression. Many existing studies have achieved good performance in non-semantic clone, but semantic clone is still a challenging task. Recently, several works have used tree or graph, such as Abstract Syntax Tree (AST), Control Flow Graph (CFG) or Program Dependency Graph (PDG) to extract semantic information from source codes. In order to reduce the complexity of tree and graph, some studies transform them into node sequences. However, this transformation will lose some semantic information. To address this issue, we propose a novel high-performance method that utilizes community detection to extract features of AST while preserving its semantic information. First, based on the AST of source code, we exploit community detection to split AST into different subtrees to extract the underlying semantics information of different code blocks, and use centrality analysis to quantify the semantic information as the weight of AST nodes. Then, the AST is converted into a sequence of tokens with weights, and a Siamese neural network model is used to detect the similarity of token sequences for semantic code clone detection. Finally, to evaluate our approach, we conduct experiments on two standard benchmark datasets, Google Code Jam (GCJ) and BigCloneBench (BCB). Experimental results show that our model outperforms the eight publicly available state-of-the-art methods in detecting code clones. It is five times faster than the tree-based method (ASTNN) in terms of time complexity. [ABSTRACT FROM AUTHOR]

In recent years, sensor technology has been widely used in the defense and control of sensitive areas in cities, or in various scenarios such as early warning of forest fires, monitoring of forest pests and diseases, and protection of endangered animals. Deploying sensors to collect data and then utilizing unmanned aerial vehicle (UAV) to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method. The current strategies for efficient data collection in above scenarios are still imperfect, and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection. With regards this, this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering, multi-UAV mission area allocation, and sub-area data acquisition scheme optimization. The improved α-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors, and the K-Dimensional method is used to form a multi-UAV cooperative workspace, and then, the genetic algorithm is used to trade-off the speed with the age of information (AoI) of the collected information and the energy consumption to form the multi-UAV data collection operation scheme. The combined optimization scheme in paper improves the performance by 95.56% and 58.21%, respectively, compared to the traditional baseline model. In order to verify the excellent generalization and applicability of the proposed method in real scenarios, the simulation test is conducted by introducing the digital elevation model data of the real terrain, and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of ±10%. Then, the advantages and disadvantages of the present method with the existing mainstream schemes are tested, and the results show that the present method has a huge advantage in terms of space and time complexity, and at the same time, the accuracy for data extraction is relatively improved by 10.46% and 12.71%. Finally, by eliminating the clustering process and the subtask assignment process, the AoI performance decreases by 3.46× and 4.45×, and the energy performance decreases by 3.52× and 4.47×. This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations. [ABSTRACT FROM AUTHOR]

The security of the wireless sensor network-Internet of Things (WSN-IoT) network is more challenging due to its randomness and self-organized nature. Intrusion detection is one of the key methodologies utilized to ensure the security of the network. Conventional intrusion detection mechanisms have issues such as higher misclassification rates, increased model complexity, insignificant feature extraction, increased training time, increased run time complexity, computation overhead, failure to identify new attacks, increased energy consumption, and a variety of other factors that limit the performance of the intrusion system model. In this research a security framework for WSN-IoT, through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet (MF_AdaDenseNet) and is benchmarked with datasets like NSL-KDD, UNSWNB15, CIDDS-001, Edge IIoT, Bot IoT. In this, the optimal feature selection using Capturing Dingo Optimization (CDO) is devised to acquire relevant features by removing redundant features. The proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO algorithm. This results in enhanced Detection Capacity with minimal computation complexity, as well as a reduction in False Alarm Rate (FAR) due to the consideration of classification error in the fitness estimation. As a result, the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques, achieving maximal Detection Capacity, precision, recall, and F-Measure of 99.46%, 99.54%, 99.91%, and 99.68%, respectively, along with minimal FAR and Mean Absolute Error (MAE) of 0.9% and 0.11. [ABSTRACT FROM AUTHOR]

In this article, we provide a unified and simplified approach to derandomize central results in the area of fault-tolerant graph algorithms. Given a graph \(G\) , a vertex pair \((s,t)\in V(G)\times V(G)\) , and a set of edge faults \(F\subseteq E(G)\) , a replacement path \(P(s,t,F)\) is an \(s\) - \(t\) shortest path in \(G\setminus F\). For integer parameters \(L,f\) , a replacement path covering (\(\mathsf{RPC}\)) is a collection of subgraphs of \(G\) , denoted by \(\mathcal{G}_{L,f}=\{G_{1},\ldots,G_{r}\}\) , such that for every set \(F\) of at most \(f\) faults (i.e., \(|F|\leq f\)) and every replacement path \(P(s,t,F)\) of at most \(L\) edges, there exists a subgraph \(G_{i}\in\mathcal{G}_{L,f}\) that contains all the edges of \(P\) and does not contain any of the edges of \(F\). The covering value of the \(\mathsf{RPC}\) \(\mathcal{G}_{L,f}\) is then defined to be the number of subgraphs in \(\mathcal{G}_{L,f}\). In the randomized setting, it is easy to build an \((L,f)\) - \(\mathsf{RPC}\) with covering value of \(O(\max\{L,f\}^{\min\{L,f\}}\cdot\min\{L,f\}\cdot \log n)\) , but to this date, there is no efficient deterministic algorithm with matching bounds. As noted recently by Alon et al. (ICALP 2019), this poses the key barrier for derandomizing known constructions of distance sensitivity oracles and fault-tolerant spanners. We show the following: — There exist efficient deterministic constructions of \((L,f)\) - \(\mathsf{RPC}\) s whose covering values almost match the randomized ones, for a wide range of parameters. Our time and value bounds improve considerably over the previous construction of Parter (DISC 2019). Our algorithms are based on the introduction of a novel notion of hash families that we call HM hash families. We then show how to construct these hash families from (algebraic) error correcting codes such as Reed–Solomon codes and Algebraic-Geometric codes. — For every \(L,f\) , and \(n\) , there exists an \(n\) -vertex graph \(G\) whose \((L,f)\) - \(\mathsf{RPC}\) covering value is \(\Omega(L^{f})\). This lower bound is obtained by exploiting connections to the problem of designing sparse fault-tolerant breadth first search (BFS) structures. An application of our above deterministic constructions is the derandomization of the algebraic construction of the distance sensitivity oracle by Weimann and Yuster (FOCS 2010). The preprocessing and query time of our deterministic algorithm nearly match the randomized bounds. This resolves the open problem of Alon et al. (ICALP 2019). Additionally, we show a derandomization of the randomized construction of vertex fault-tolerant spanners by Dinitz and Krauthgamer (PODC 2011) and Braunschvig et al. (Theor. Comput. Sci., 2015). The time complexity and the size bounds of the output spanners nearly match the randomized counterparts. [ABSTRACT FROM AUTHOR]

Longest common substring (LCS) is an important text processing problem, which has recently been investigated in the quantum query model. The decision version of this problem, LCS with threshold \(d\) , asks whether two length- \(n\) input strings have a common substring of length \(d\). The two extreme cases, \(d=1\) and \(d=n\) , correspond, respectively to Element Distinctness and Unstructured Search, two fundamental problems in quantum query complexity. However, the intermediate case \(1\ll d\ll n\) was not fully understood. We show that the complexity of LCS with threshold \(d\) smoothly interpolates between the two extreme cases up to \(n^{o(1)}\) factors: — LCS with threshold \(d\) has a quantum algorithm in \(n^{2/3+o(1)}/d^{1/6}\) query complexity and time complexity, and requires at least \(\Omega(n^{2/3}/d^{1/6})\) quantum query complexity. Our result improves upon previous upper bounds \(\widetilde{O}(\min\{n/d^{1/2},n^{2/3}\})\) (Le Gall and Seddighin ITCS 2022, Akmal and Jin SODA 2022), and answers an open question of Akmal and Jin. Our main technical contribution is a quantum speed-up of the powerful String Synchronizing Set technique introduced by Kempa and Kociumaka (STOC 2019). It consistently samples \(n/\tau^{1-o(1)}\) synchronizing positions in the string depending on their length- \(\Theta(\tau)\) contexts, and each synchronizing position can be reported by a quantum algorithm in \(\widetilde{O}(\tau^{1/2+o(1)})\) time. Our quantum string synchronizing set also yields a near-optimal LCE data structure in the quantum setting. As another application of our quantum string synchronizing set, we study the \(k\) -mismatch Matching problem, which asks if the pattern has an occurrence in the text with at most \(k\) Hamming mismatches. Using a structural result of Charalampopoulos et al. (FOCS 2020), we obtain: — \(k\) -mismatch matching has a quantum algorithm with \(k^{3/4}n^{1/2+o(1)}\) query complexity and \(\widetilde{O}(kn^{1/2})\) time complexity. We also observe a non-matching quantum query lower bound of \(\Omega(\sqrt{kn})\). [ABSTRACT FROM AUTHOR]

Recently neural style transfer (NST) has drawn a lot of interest of researchers, with notable advancements in color representation, texture, speed, and image quality. While previous studies focused on transferring artistic style across entire content images, a new approach proposes to transfer style specifically to objects within the content image based on the style image and maintain photorealism. Recent techniques have produced intriguing creative effects, but often only work with artificial effects, leaving real flaws visible in photographs used as references for styles. The suggested approach employs a two-dimensional wavelet transform (WT) to achieve style transfer by adjusting image structure with high-pass and low pass filters (LPF). Preserving the information content and numerical attributes of VGGNet19 through WT-based style transfer using the db5 WT at level 5, we can achieve a peak signal-to-noise ratio (PSNR) value of up to 96.76725. The qualitative result of the proposed methodology is compared with other existing algorithm. Also, the time complexity of the proposed methodology on different hardware platforms has been calculated and presented in the paper. The proposed methodology able to maintains appealing and precise quality of resultant image. [ABSTRACT FROM AUTHOR]

Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The 'intrinsic predictability' of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise.We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie (Pygoscelis adeliae) and emperor penguin (Aptenodytes forsteri) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species.We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%).rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of the inherent limitations to a system's predictability, it can guide a modeller's expectations for forecast performance. [ABSTRACT FROM AUTHOR]

Most existing knowledge graphs (KGs) suffer from incompleteness, which will be detrimental to a variety of downstream applications. Link prediction is the task of predicting missing links in the KGs and can effectively address the issue of incompleteness by knowledge graph embedding (KGE). ConvE, a relatively popular KGE model based on convolutional neural networks, has shown superiority in link prediction. Some subsequent extension models of ConvE achieve state-of-the-art performance by increasing complexity and training time, which result in a high risk of overfitting and a limited performance due to the large number of parameters concentrated in the fully connected projection layer. To address these challenges, we for the first time innovatively introduce and extend a recently simple network architecture gMLP (based on multi-layer perceptrons MLPs with gating) in vision applications for link prediction. We propose a simple and efficient model called gMLP-KGE, which consists of an embedding layer, an input layer, an extended gMLP layer, and an output layer. Extensive experiments show that the number of parameters of gMLP-KGE is close to that of ConvE and less than other extension models, while gMLP-KGE consistently performs well on seven datasets of different scales under most evaluation metrics. [ABSTRACT FROM AUTHOR]

A hybrid structure is an arrangement that makes use of many hierarchical reporting structures and is applied to algebraic structures such as groups and rings. In the discipline of abstract algebra, an ideal of a near-ring is a unique subset of its elements in ring theory. Ideals generalize specific subsets of integers, such as even numbers or multiples of three. Researchers have been using mathematical theories of fuzzy sets in ring theory to explain the uncertainties that emerge in various domains such as art and science, engineering, medical science, and in environment. By fusing soft sets and fuzzy sets, a new mathematical tool that has significant advantages in dealing with uncertain information is provided. Consequently, there is always some discrepancy between reality's haziness and its mathematical model's precision. Hence ring theory has been widely used in many researches but there is some uncertainty in converting the fuzzy sets to a hybrid structure of any algebraic structure. Many approaches were done in groups. Therefore, the Hybrid structure of fuzzy sets in near rings is introduced, in which the fuzzy ideals are converted to hybrid ideals and fuzzy maximal ideals are converted to hybrid maximal ideals. For hybridization, firstly the hybrid structure is established and then sub-near rings and near rings are also determined. Then the hybrid structure of sub-near rings and ideals is introduced. This converts the fuzzy ideals and fuzzy maximal ideals to hybrid ideals and hybrid maximal ideals. The result obtained by the proposed model efficiently solved the uncertainty problems and the effectiveness of the proposed approach shows the best class, mean, worst class, and time complexity. [ABSTRACT FROM AUTHOR]

Shared autonomous electric vehicles (SAEVs) have recently attracted significant public interest. The dynamic ride-sharing using SAEVs appears to have the advantages of reducing travel costs and relieving urban traffic congestion. It is meant to improve the practical application value of the dynamic ride-sharing mode of SAEVs. In this paper, to reduce the solution time complexity, a pre-matching algorithm considering the driverless and charging characteristics of SAEVs is developed, and then a two-stage, graph-based many-to-one ride-matching (GMOM) algorithm is proposed for the dynamic ride-sharing problem in the Autonomous Mobility-on-Demand system (AMOD). The dataset from DiDi during the peak travel time and the real-time traffic flow from the AutoNavi map were used to verify the effects of the method. The results demonstrate that the GMOM approach can effectively reduce computational complexity and improve user satisfaction. The dynamic ride-sharing mode based on the GMOM algorithm has a 5.67% higher service rate and 45.56% more vehicle calls than the non-ride-sharing mode under the same conditions. It is found that the cost-effectiveness of using ride-sharing services is relatively high for 10–20 km trips during peak travel time and the dynamic ride-sharing may extend total travel time but will reduce passengers' waiting time. [ABSTRACT FROM AUTHOR]

Mobile agents are agents that can migrate from host-to-host to work in a heterogeneous network environment. A mobile agent can migrate from host-to-host in its plan with the statistics generated on each host through a route known as migration pattern. Migration pattern therefore is the route the agents use to travel within the plan from the first host to the last host. However, there is a need for a comparison between the commonly used optimization algorithms in developing migration patterns for mobile agents with respect to some evaluation metrics. In this paper, the three techniques firefly algorithm (FFA), honeybee optimization (HBO) and particle swarm optimization (PSO) were used for developing migration patterns for mobile agents and their comparison was done based on migration time, time complexity and network load as metrics. PSO is discovered to perform better in terms of network load with an average of 242.3905 bits per second (bps), time complexity with an average of 41.2688 number of nodes (n), and migration/transmission time with an average of 4.203462 seconds (s). [ABSTRACT FROM AUTHOR]

Leukemia, a cancer that attacks human white blood cells, is one of the deadliest illnesses. Detecting affected cells in microscopic images becomes tedious because feature variants are not predicted correctly by a hematologist. Therefore image handling techniques failed to select the importance of the features scaling counts, entities, and precise size and shape of cells presented in the microscopic image. To resolve this problem, Deep Spectral Convolution Neural Network (DSCNN) based on Leukemia cancer detection using Invariant Entity Scalar Feature Selection (IESFS) is proposed to identify the risk factor of cancer for early diagnosis. Initially, preprocessing is carried out using cascade Gabor filters. Based on Structural Cascade Segmentation (SCS), the white blood cell regions are categorized into affected and non-affected margins and verify the edges using canny edge mapping. This estimates the scaling cell size, counts, entities and angular cell projection of weights from each segmented feature region. Then find the entity relation of cell projection equivalence using Color Intensive Histogram Equalization (CIHE). After segmenting the angular vector, projection scaling is applied to correlate the entity's object scaling comparator. Then scaling features were selected using Invariant Entity Scalar Feature Selection (IESFS) by averaging the mean depth values of feature weight and trained with a deep convolution neural network for predicting max equivalence entity weights for finding the affected cells and counts in microscopic images. This improves the prediction of cancer cell accuracy as well high performance in sensitivity 92.7 %, specificity 92.3 %, and f-measure 93.6 % with redundant time complexity. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) have achieved excellent performances in many graph-related tasks. However, they need appropriate pooling operations to deal with the graph classification tasks, and thus, they may suffer from some limitations such as information loss and ignorance of the part-whole relationships. CapsGNN is proposed to solve the above-mentioned issues, but suffers from high time and space complexities leading to its poor scalability. In this paper, we propose a novel, effective and efficient graph capsule network called LightCapsGNN. First, we devise a fast voting mechanism (called LightVoting) implemented via linear combinations of K shared transformation matrices to reduce the number of trainable parameters in the voting procedure. Second, an improved reconstruction layer is proposed to encourage our model to capture more informative and essential knowledge of the input graph. Third, other improvements are combined to further accelerate our model, e.g., matrix capsules and a trainable routing mechanism. Finally, extensive experiments are conducted on the popular real-world graph benchmarks in the graph classification tasks and the proposed model can achieve competitive or even better performance compared to ten baselines or state-of-the-art models. Furthermore, compared to other CapsGNNs, the proposed model reduce almost 99 % learnable parameters and 31.1 % running time. [ABSTRACT FROM AUTHOR]

The high dimensionality of text data is a challenging issue that requires efficient methods to reduce vector space and improve classification accuracy. Existing filter-based methods fail to address the redundancy issue, resulting in the selection of irrelevant and redundant features. Information theory-based methods effectively solve this problem but are not practical for large amounts of data due to their high time complexity. The proposed method, termed semantic similarity-aware feature selection and redundancy removal (SS-FSRR), employs joint mutual information between the pairs of semantically related terms and the class label to capture redundant features. It is predicated on the assumption that semantically related terms imply potentially redundant ones, which can significantly reduce execution time by avoiding sequential search strategies. In this work, we use Word2Vec's CBOW model to obtain semantic similarity between terms. The efficiency of the SS-FSRR is compared to six state-of-the-art competitive selection methods for categorical data using two traditional classifiers (SVM and NB) and a robust deep learning model (LSTM) on seven datasets with 10-fold cross-validation, where experimental results show that the SS-FSRR outperforms the other methods on most tested datasets with high stability as measured by the Jaccard's Index. [ABSTRACT FROM AUTHOR]

Parameterized complexity, introduced to efficiently solve NP‐hard problems for small values of a fixed parameter, has been recently used as a tool to speed up algorithms for tractable problems. Following this line of research, we design algorithms parameterized by neighborhood diversity (nd$$ \mathsf{nd} $$) for several graph theoretic problems in P$$ P $$: Maximumb$$ b $$‐Matching, Triangle Counting and Listing, Girth, Global Minimum Vertex Cut, and Perfect Graphs Recognition. Such problems are known to admit algorithms parameterized by modular‐width (mw$$ \mathsf{mw} $$) and consequently—as nd$$ \mathsf{nd} $$ is a special case of mw$$ \mathsf{mw} $$—by nd$$ \mathsf{nd} $$. However, the proposed novel algorithms allow for improving the computational complexity from time O(f(mw)·n+m)$$ O\left(f\left(\mathsf{mw}\right)\cdotp n+m\right) $$—where n$$ n $$ and m$$ m $$ denote, respectively, the number of vertices and edges in the input graph—to time O(g(nd)+n+m)$$ O\left(g\left(\mathsf{nd}\right)+n+m\right) $$ which is only additive in the size of the input. Then we consider some classical NP‐hard problems (Maximum independent set, Maximum clique, and Minimum dominating set) and show that for several classes of hereditary graphs, they admit linear time algorithms for sufficiently small—nonnecessarily constant—values of the neighborhood diversity parameter. [ABSTRACT FROM AUTHOR]

Objective: Alzheimer's disease (AD) is a type of neurological illness that significantly impacts individuals' daily lives. In the intelligent diagnosis of AD, 3D networks require larger computational resources and storage space for training the models, leading to increased model complexity and training time. On the other hand, 2D slices analysis may overlook the 3D structural information of MRI and can result in information loss. Approach: We propose a multi-slice attention fusion and multi-view personalized fusion lightweight network for automated AD diagnosis. It incorporates a multi-branch lightweight backbone to extract features from sagittal, axial, and coronal view of MRI, respectively. In addition, we introduce a novel multi-slice attention fusion module, which utilizes a combination of global and local channel attention mechanism to ensure consistent classification across multiple slices. Additionally, a multi-view personalized fusion module is tailored to assign appropriate weights to the three views, taking into account the varying significance of each view in achieving accurate classification results. To enhance the performance of the multi-view personalized fusion module, we utilize a label consistency loss to guide the model's learning process. This encourages the acquisition of more consistent and stable representations across all three views. Main results: The suggested strategy is efficient in lowering the number of parameters and FLOPs, with only 3.75M and 4.45G respectively, and accuracy improved by 10.5% to 14% in three tasks. Moreover, in the classification tasks of AD vs. CN, AD vs. MCI and MCI vs. CN, the accuracy of the proposed method is 95.63%, 86.88% and 85.00%, respectively, which is superior to the existing methods. Conclusions: The results show that the proposed approach not only excels in resource utilization, but also significantly outperforms the four comparison methods in terms of accuracy and sensitivity, particularly in detecting early-stage AD lesions. It can precisely capture and accurately identify subtle brain lesions, providing crucial technical support for early intervention and treatment. [ABSTRACT FROM AUTHOR]

Quantum computers can be used for efficient storage, processing and transmission of digital images. It involves exploitation of quantum properties to represent and process the images in a quantum computer. The first important step to process images in a quantum computer is the representation of images in quantum states. In this paper, we introduce two efficient implementation of algorithms to store the bit-planes of an image in quantum states, one for grayscale images and other for color images. The proposed implementations can be used to improve the effectiveness of image processing algorithms such as color complement operation, edge detection, image compression and so on. The proposed implementation has 10% less quantum cost and four times less time complexity compared to BRQI model for greyscale images, and 30% less quantum cost and four times less time complexity compared to QRCI model for color images. [ABSTRACT FROM AUTHOR]

The online trading market has been greatly improved by the significance of Cross-Chain (CC) transactions. However, malicious events are the chief threat to offering secure cross-chain transactions; several crypto security models have been executed in the past to enrich the CC transaction process. However, those models cannot provide secure CC data because of malicious harm. So, the current report aimed to implement a novel Elman Neural-based CAST Blockchain Framework (ENbCBF) to gain a secure CC platform. Firstly, the malicious prediction functions were executed to maintain the CC's confidential score. Consequently, the transaction process was begun in the Ethereum blockchain environment. Hence, the planned novel secure CC design is validated in the etherscan Python environment. The User needs to decide the transaction amount types; the Elman neural function continuously afforded the attack recognition process, resulting in less time complexity by avoiding the delay. Hence, the reported high malicious event recognition exactness and less processing time for the transaction and crypto process than conventional studies. [ABSTRACT FROM AUTHOR]